Résumé In semi-arid regions water resources are limited and hereby the available groundwater for irrigation
and other water uses are severely constrained. The hydrological regime in these areas is extreme
and highly variable mainly due to rainfall patterns characterized by events of short duration and
high intensities and large heterogeneity of the landscape. It is necessary to understand the
mechanisms and driving forces behind each component of the hydrological cycle to allow for
reliable estimates of the overall water resources.
In the present thesis the physically-based distributed MIKE SHE model code has been applied to
the Andarax River basin in south-east Spain to investigate the hydrological behavior of the basin. A
special research focus has been to develop methods for estimating the spatial and temporal
distribution of evapotranspiration using remote sensing data.
In the first study (reported in appendix A) a conventional MIKE SHE model was constructed,
calibrated and validated. The availability of traditional geological and hydrological data for the
catchment is rather sparse and the hydrological responses and the significance of the different
components are not known in details. As a result of this four different calibration and validation
scenarios were carried out. The results show larger discrepancies between observed and simulated
discharge than usually seen for more temperate hydrological regions. Including snow accumulation
and melting in the model increases the model performance in one part of the catchment but for the
entire Andarax River basin snow has little impact on the overall water balance.
The improvement of the quantity, quality and the resolution of remote sensing data have progressed
over the last decades and hereby the perspectives of integrate remote sensing data into hydrological
studies have also increased. The second study (reported in appendix B) investigates the perspectives
in using a complex Soil Vegetation Atmosphere Transfer (SVAT) model implemented in the MIKE
SHE code to improve the spatial and temporal distribution of the output results (e.g.
evapotranspiraiton and recharge). Important variables (leaf area index, albedo, land surface
temperature and global radiation) are derived from remote sensing and used as input to the MIKE
SHE SVAT model. The results illustrate that the model performance was not improved notably by
using the MIKE SHE SVAT model compared with the result obtained from the conventional MIKE
SHE model (appendix A). However the analysis documented that using remote sensing data for
driving a SVAT model is a useful approach.
The final study (reported in appendix C) looks at the possibilities of using simpler methods driven
by remote sensing data to predict the spatio-temporal variation in the evapotranspiration. The
triangle method and SEBAL were chosen for investigation. These two methods were compared with
the result from the conventional MIKE SHE model and the MIKE SHE SVAT model. The results
showed a wide range of estimated evapotranspiration between the methods. Each method is based
on a number of assumptions that evidently have some limitations ; this will lead to discrepancies in
the simulation results and a clear judgment as to which methods produce the most accurate result is
therefore difficult to make.